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Abstract
Four-dimensional COAMPS dynamic initialization (FCDI) analyses with high temporal and spatial resolution GOES-16 atmospheric motion vectors (AMVs) are utilized to analyze the development and rapid intensification of a mesovortex about 150 km to the south of the center of the subtropical cyclone, Cyclone Henri (2021). During the period of the unusual Henri westward track along 30°N, the FCDI z = 300-m wind vector analyses demonstrate highly asymmetric wind fields and a horseshoe-shaped isotach maximum that is about 75 km from the center, which are characteristics more consistent with the definition of a subtropical cyclone than of a tropical cyclone. Furthermore, the Henri westward track and the vertical wind shear have characteristics resembling a Rossby wave breaking conceptual model. The GOES-16 mesodomain AMVs allow the visualization of a series of outflow bursts in space and time in association with the southern mesovortex development and intensification. Then the FCDI analyses forced by those thousands of AMVs each 15 min depict the z = 13 910-m wind field responses and the subsequent z = 300-m wind field adjustments in the southern mesovortex. A second northern outflow burst displaced to the southeast of the main Henri vortex also led to a strong low-level mesovortex. It was when the two outflow bursts joined to create an eastward radial outflow all along the line between them that the southern mesovortex reached maximum intensity and maximum size. In contrast to the numerical model predictions of intensification, outflow from the mesovortex directed over the main Henri vortex led to a decrease in intensity.
Abstract
Four-dimensional COAMPS dynamic initialization (FCDI) analyses with high temporal and spatial resolution GOES-16 atmospheric motion vectors (AMVs) are utilized to analyze the development and rapid intensification of a mesovortex about 150 km to the south of the center of the subtropical cyclone, Cyclone Henri (2021). During the period of the unusual Henri westward track along 30°N, the FCDI z = 300-m wind vector analyses demonstrate highly asymmetric wind fields and a horseshoe-shaped isotach maximum that is about 75 km from the center, which are characteristics more consistent with the definition of a subtropical cyclone than of a tropical cyclone. Furthermore, the Henri westward track and the vertical wind shear have characteristics resembling a Rossby wave breaking conceptual model. The GOES-16 mesodomain AMVs allow the visualization of a series of outflow bursts in space and time in association with the southern mesovortex development and intensification. Then the FCDI analyses forced by those thousands of AMVs each 15 min depict the z = 13 910-m wind field responses and the subsequent z = 300-m wind field adjustments in the southern mesovortex. A second northern outflow burst displaced to the southeast of the main Henri vortex also led to a strong low-level mesovortex. It was when the two outflow bursts joined to create an eastward radial outflow all along the line between them that the southern mesovortex reached maximum intensity and maximum size. In contrast to the numerical model predictions of intensification, outflow from the mesovortex directed over the main Henri vortex led to a decrease in intensity.
The coverage and quality of remotely sensed upper-tropospheric moisture parameters have improved considerably with the deployment of a new generation of operational geostationary meteorological satellites: GOES-8/9 and GMS-5. The GOES-8/9 water vapor imaging capabilities have increased as a result of improved radiometric sensitivity and higher spatial resolution. The addition of a water vapor sensing channel on the latest GMS permits nearly global viewing of upper-tropospheric water vapor (when joined with GOES and Meteosat) and enhances the commonality of geostationary meteorological satellite observing capabilities. Upper-tropospheric motions derived from sequential water vapor imagery provided by these satellites can be objectively extracted by automated techniques. Wind fields can be deduced in both cloudy and cloud-free environments. In addition to the spatially coherent nature of these vector fields, the GOES-8/9 multispectral water vapor sensing capabilities allow for determination of wind fields over multiple tropospheric layers in cloud-free environments. This article provides an update on the latest efforts to extract water vapor motion displacements over meteorological scales ranging from subsynoptic to global. The potential applications of these data to impact operations, numerical assimilation and prediction, and research studies are discussed.
The coverage and quality of remotely sensed upper-tropospheric moisture parameters have improved considerably with the deployment of a new generation of operational geostationary meteorological satellites: GOES-8/9 and GMS-5. The GOES-8/9 water vapor imaging capabilities have increased as a result of improved radiometric sensitivity and higher spatial resolution. The addition of a water vapor sensing channel on the latest GMS permits nearly global viewing of upper-tropospheric water vapor (when joined with GOES and Meteosat) and enhances the commonality of geostationary meteorological satellite observing capabilities. Upper-tropospheric motions derived from sequential water vapor imagery provided by these satellites can be objectively extracted by automated techniques. Wind fields can be deduced in both cloudy and cloud-free environments. In addition to the spatially coherent nature of these vector fields, the GOES-8/9 multispectral water vapor sensing capabilities allow for determination of wind fields over multiple tropospheric layers in cloud-free environments. This article provides an update on the latest efforts to extract water vapor motion displacements over meteorological scales ranging from subsynoptic to global. The potential applications of these data to impact operations, numerical assimilation and prediction, and research studies are discussed.
Cloud-drift winds have been produced from geostationary satellite data in the Western Hemisphere since the early 1970s. During the early years, winds were used as an aid for the short-term forecaster in an era when numerical forecasts were often of questionable quality, especially over oceanic regions. Increased computing resources over the last two decades have led to significant advances in the performance of numerical forecast models. As a result, continental forecasts now stand to gain little from the inspection or assimilation of cloud-drift wind fields. However, the oceanic data void remains, and although numerical forecasts in such areas have improved, they still suffer from a lack of in situ observations. During the same two decades, the quality of geostationary satellite data has improved considerably, and the cloud-drift wind production process has also benefited from increased computing power. As a result, fully automated wind production is now possible, yielding cloud-drift winds whose quality and quantity is sufficient to add useful information to numerical model forecasts in oceanic and coastal regions. This article will detail the automated cloud-drift wind production process, as operated by the National Environmental Satellite Data and Information Service within the National Oceanic and Atmospheric Administration.
Cloud-drift winds have been produced from geostationary satellite data in the Western Hemisphere since the early 1970s. During the early years, winds were used as an aid for the short-term forecaster in an era when numerical forecasts were often of questionable quality, especially over oceanic regions. Increased computing resources over the last two decades have led to significant advances in the performance of numerical forecast models. As a result, continental forecasts now stand to gain little from the inspection or assimilation of cloud-drift wind fields. However, the oceanic data void remains, and although numerical forecasts in such areas have improved, they still suffer from a lack of in situ observations. During the same two decades, the quality of geostationary satellite data has improved considerably, and the cloud-drift wind production process has also benefited from increased computing power. As a result, fully automated wind production is now possible, yielding cloud-drift winds whose quality and quantity is sufficient to add useful information to numerical model forecasts in oceanic and coastal regions. This article will detail the automated cloud-drift wind production process, as operated by the National Environmental Satellite Data and Information Service within the National Oceanic and Atmospheric Administration.
Abstract
The objective in this study is to demonstrate how two unique datasets from the Tropical Cyclone Intensity (TCI-15) field experiment can be used to diagnose the environmental and internal factors contributing to the interruption of the rapid decay of Hurricane Joaquin (2015) and then a subsequent 30-h period of constant intensity. A special CIMSS vertical wind shear (VWS) dataset reprocessed at 15-min intervals provides a more precise documentation of the large (~15 m s−1) VWS throughout most of the rapid decay period, and then the timing of a rapid decrease in VWS to moderate (~8 m s−1) values prior to, and following, the rapid decay period. During this period, the VWS was moderate because Joaquin was between large VWSs to the north and near-zero VWSs to the south, which is considered to be a key factor in how Joaquin was able to be sustained at hurricane intensity even though it was moving poleward over colder water. A unique dataset of High Definition Sounding System (HDSS) dropwindsondes deployed from the NASA WB-57 during the TCI-15 field experiment is utilized to calculate zero-wind centers during Joaquin center overpasses that reveal for the first time the vortex tilt structure through the entire troposphere. The HDSS datasets are also utilized to calculate the inertial stability profiles and the inner-core potential temperature anomalies in the vertical. Deeper lower-tropospheric layers of near-zero vortex tilt are correlated with stronger storm intensities, and upper-tropospheric layers with large vortex tilts due to large VWSs are correlated with weaker storm intensities.
Abstract
The objective in this study is to demonstrate how two unique datasets from the Tropical Cyclone Intensity (TCI-15) field experiment can be used to diagnose the environmental and internal factors contributing to the interruption of the rapid decay of Hurricane Joaquin (2015) and then a subsequent 30-h period of constant intensity. A special CIMSS vertical wind shear (VWS) dataset reprocessed at 15-min intervals provides a more precise documentation of the large (~15 m s−1) VWS throughout most of the rapid decay period, and then the timing of a rapid decrease in VWS to moderate (~8 m s−1) values prior to, and following, the rapid decay period. During this period, the VWS was moderate because Joaquin was between large VWSs to the north and near-zero VWSs to the south, which is considered to be a key factor in how Joaquin was able to be sustained at hurricane intensity even though it was moving poleward over colder water. A unique dataset of High Definition Sounding System (HDSS) dropwindsondes deployed from the NASA WB-57 during the TCI-15 field experiment is utilized to calculate zero-wind centers during Joaquin center overpasses that reveal for the first time the vortex tilt structure through the entire troposphere. The HDSS datasets are also utilized to calculate the inertial stability profiles and the inner-core potential temperature anomalies in the vertical. Deeper lower-tropospheric layers of near-zero vortex tilt are correlated with stronger storm intensities, and upper-tropospheric layers with large vortex tilts due to large VWSs are correlated with weaker storm intensities.
The first Advanced Microwave Sounding Unit (AMSU) was launched aboard the NOAA-15 satellite on 13 May 1998. The AMSU is well suited for the observation of tropical cyclones because its measurements are not significantly affected by the ice clouds that cover tropical storms. In this paper, the following are presented: 1) upper-tropospheric thermal anomalies in tropical cyclones retrieved from AMSU data, 2) the correlation of maximum temperature anomalies with maximum wind speed and central pressure, 3) winds calculated from the temperature anomaly field, 4) comparison of AMSU data with GOES and AVHRR imagery, and 5) tropical cyclone rainfall potential. The AMSU data appear to offer substantial opportunities for improvement in tropical cyclone analysis and forecasting.
The first Advanced Microwave Sounding Unit (AMSU) was launched aboard the NOAA-15 satellite on 13 May 1998. The AMSU is well suited for the observation of tropical cyclones because its measurements are not significantly affected by the ice clouds that cover tropical storms. In this paper, the following are presented: 1) upper-tropospheric thermal anomalies in tropical cyclones retrieved from AMSU data, 2) the correlation of maximum temperature anomalies with maximum wind speed and central pressure, 3) winds calculated from the temperature anomaly field, 4) comparison of AMSU data with GOES and AVHRR imagery, and 5) tropical cyclone rainfall potential. The AMSU data appear to offer substantial opportunities for improvement in tropical cyclone analysis and forecasting.
Abstract
A novel application of numerical weather prediction (NWP) models within an end-to-end processing system used to demonstrate advanced hyperspectral satellite technologies and instrument concepts is presented. As part of this system, sophisticated NWP models are used to generate simulated atmospheric profile datasets with fine horizontal and vertical resolution. The simulated datasets, which are treated as the “truth” atmosphere, are subsequently passed through a sophisticated forward radiative transfer model to generate simulated top-of-atmosphere (TOA) radiances across a broad spectral region. Atmospheric motion vectors and temperature and water vapor retrievals generated from the TOA radiances are then compared with the original model-simulated atmosphere to demonstrate the potential utility of future hyperspectral wind and retrieval algorithms. Representative examples of TOA radiances, atmospheric motion vectors, and temperature and water vapor retrievals are shown to illustrate the use of the simulated datasets.
Case study results demonstrate that the numerical models are able to realistically simulate mesoscale cloud, temperature, and water vapor structures present in the real atmosphere. Because real hyperspectral radiance measurements with high spatial and temporal resolution are not available for large geographical domains, the simulated TOA radiance datasets are the only viable alternative that can be used to demonstrate the new hyperspectral technologies and capabilities. As such, sophisticated mesoscale models are critically important for the demonstration of the future end-to-end processing system.
Abstract
A novel application of numerical weather prediction (NWP) models within an end-to-end processing system used to demonstrate advanced hyperspectral satellite technologies and instrument concepts is presented. As part of this system, sophisticated NWP models are used to generate simulated atmospheric profile datasets with fine horizontal and vertical resolution. The simulated datasets, which are treated as the “truth” atmosphere, are subsequently passed through a sophisticated forward radiative transfer model to generate simulated top-of-atmosphere (TOA) radiances across a broad spectral region. Atmospheric motion vectors and temperature and water vapor retrievals generated from the TOA radiances are then compared with the original model-simulated atmosphere to demonstrate the potential utility of future hyperspectral wind and retrieval algorithms. Representative examples of TOA radiances, atmospheric motion vectors, and temperature and water vapor retrievals are shown to illustrate the use of the simulated datasets.
Case study results demonstrate that the numerical models are able to realistically simulate mesoscale cloud, temperature, and water vapor structures present in the real atmosphere. Because real hyperspectral radiance measurements with high spatial and temporal resolution are not available for large geographical domains, the simulated TOA radiance datasets are the only viable alternative that can be used to demonstrate the new hyperspectral technologies and capabilities. As such, sophisticated mesoscale models are critically important for the demonstration of the future end-to-end processing system.
Abstract
Atmospheric motion vectors (AMVs) are derived from satellite-observed motions of clouds and water vapor features. They provide crucial information in regions void of conventional observations and contribute to forecaster diagnostics of meteorological conditions, as well as numerical weather prediction. AMVs derived from geostationary (GEO) satellite observations over the middle latitudes and tropics have been utilized operationally since the 1980s; AMVs over the polar regions derived from low‐earth (polar)‐orbiting (LEO) satellites have been utilized since the early 2000s. There still exists a gap in AMV coverage between these two sources in the latitude band poleward of 60° and equatorward of 70° (both hemispheres). To address this AMV gap, the use of a novel approach to create image sequences that consist of composites derived from a combination of LEO and GEO observations that extend into the deep middle latitudes is explored. Experiments are performed to determine whether the satellite composite images can be employed to generate AMVs over the gap regions. The derived AMVs are validated over both the Southern Ocean/Antarctic and the Arctic gap regions over a multiyear period using rawinsonde wind observations. In addition, a two-season numerical model impact study using the Global Forecast System indicates that the assimilation of these AMVs can improve upon the control (operational) forecasts, particularly during lower-skill (dropout) events.
Abstract
Atmospheric motion vectors (AMVs) are derived from satellite-observed motions of clouds and water vapor features. They provide crucial information in regions void of conventional observations and contribute to forecaster diagnostics of meteorological conditions, as well as numerical weather prediction. AMVs derived from geostationary (GEO) satellite observations over the middle latitudes and tropics have been utilized operationally since the 1980s; AMVs over the polar regions derived from low‐earth (polar)‐orbiting (LEO) satellites have been utilized since the early 2000s. There still exists a gap in AMV coverage between these two sources in the latitude band poleward of 60° and equatorward of 70° (both hemispheres). To address this AMV gap, the use of a novel approach to create image sequences that consist of composites derived from a combination of LEO and GEO observations that extend into the deep middle latitudes is explored. Experiments are performed to determine whether the satellite composite images can be employed to generate AMVs over the gap regions. The derived AMVs are validated over both the Southern Ocean/Antarctic and the Arctic gap regions over a multiyear period using rawinsonde wind observations. In addition, a two-season numerical model impact study using the Global Forecast System indicates that the assimilation of these AMVs can improve upon the control (operational) forecasts, particularly during lower-skill (dropout) events.
Abstract
This study examines the error characteristics of atmospheric motion vectors (AMVs) obtained by tracking the movement of water vapor features. A high-resolution numerical simulation of a dynamic weather event is used as a baseline, and AMVs tracked from retrieved water vapor fields are compared with the “true” winds produced by the model. The sensitivity of AMV uncertainty to time interval, AMV tracking window size, water vapor content, horizontal gradient, and wind structure is examined. AMVs are derived from the model water vapor field at a specific height and also from water vapor fields vertically blurred using smoothing functions consistent with high-spectral-resolution infrared (IR) and high-frequency microwave (MW) water vapor sounders. Uncertainties in water vapor AMVs are state dependent and are largest for regions with small water vapor content and small water vapor spatial gradient and in places where the flow runs parallel to contours of constant water vapor content. Smoothing of water vapor consistent with IR and MW retrievals does not increase AMV uncertainty; however, the yield of AMVs from IR sounders is much lower than from MW sounders because of the inability of IR sounders to retrieve water vapor below clouds. The yield and error are similar for AMVs in the lower and upper troposphere, even though the water vapor content in the upper troposphere is much smaller. The results have implications for the design of new observing systems, as well as the specification of errors when AMVs are ingested in data assimilation systems.
Abstract
This study examines the error characteristics of atmospheric motion vectors (AMVs) obtained by tracking the movement of water vapor features. A high-resolution numerical simulation of a dynamic weather event is used as a baseline, and AMVs tracked from retrieved water vapor fields are compared with the “true” winds produced by the model. The sensitivity of AMV uncertainty to time interval, AMV tracking window size, water vapor content, horizontal gradient, and wind structure is examined. AMVs are derived from the model water vapor field at a specific height and also from water vapor fields vertically blurred using smoothing functions consistent with high-spectral-resolution infrared (IR) and high-frequency microwave (MW) water vapor sounders. Uncertainties in water vapor AMVs are state dependent and are largest for regions with small water vapor content and small water vapor spatial gradient and in places where the flow runs parallel to contours of constant water vapor content. Smoothing of water vapor consistent with IR and MW retrievals does not increase AMV uncertainty; however, the yield of AMVs from IR sounders is much lower than from MW sounders because of the inability of IR sounders to retrieve water vapor below clouds. The yield and error are similar for AMVs in the lower and upper troposphere, even though the water vapor content in the upper troposphere is much smaller. The results have implications for the design of new observing systems, as well as the specification of errors when AMVs are ingested in data assimilation systems.
Abstract
New objective methods are introduced that use readily available data to estimate various aspects of the two-dimensional surface wind field structure in hurricanes. The methods correlate a variety of wind field metrics to combinations of storm intensity, storm position, storm age, and information derived from geostationary satellite infrared (IR) imagery. The first method estimates the radius of maximum wind (RMW) in special cases when a clear symmetric eye is identified in the IR imagery. The second method estimates RMW, and the additional critical wind radii of 34-, 50-, and 64-kt winds for the general case with no IR scene–type constraint. The third method estimates the entire two-dimensional surface wind field inside a storm-centered disk with a radius of 182 km. For each method, it is shown that the inclusion of infrared satellite data measurably reduces error. All of the methods can be transitioned to an operational setting or can be used as a postanalysis tool.
Abstract
New objective methods are introduced that use readily available data to estimate various aspects of the two-dimensional surface wind field structure in hurricanes. The methods correlate a variety of wind field metrics to combinations of storm intensity, storm position, storm age, and information derived from geostationary satellite infrared (IR) imagery. The first method estimates the radius of maximum wind (RMW) in special cases when a clear symmetric eye is identified in the IR imagery. The second method estimates RMW, and the additional critical wind radii of 34-, 50-, and 64-kt winds for the general case with no IR scene–type constraint. The third method estimates the entire two-dimensional surface wind field inside a storm-centered disk with a radius of 182 km. For each method, it is shown that the inclusion of infrared satellite data measurably reduces error. All of the methods can be transitioned to an operational setting or can be used as a postanalysis tool.
Abstract
A dynamic initialization assimilation scheme is demonstrated utilizing rapid-scan atmospheric motion vectors (AMVs) at 15-min intervals to simulate the real-time capability that now exists from the new generation of geostationary meteorological satellites. The impacts of these AMVs are validated with special Tropical Cyclone Intensity Experiment (TCI-15) datasets during 1200–1800 UTC 4 October leading up to a NASA WB-57 eyewall crossing of Hurricane Joaquin. Incorporating the AMV fields in the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI) COAMPS Dynamic Initialization (SCDI) means there are 30 and 90 time steps on the 15- and 5-km grids, respectively, during which the mass fields are adjusted to these AMV-based wind increments during each 15-min assimilation period. The SCDI analysis of the three-dimensional vortex structure of Joaquin at 1800 UTC 4 October closely replicates the vortex tilt analyzed from the High-Definition Sounding System (HDSS) dropwindsondes. Vertical wind shears based on the AMVs at 15-min intervals are well correlated with the extreme rapid decay, an interruption of that rapid decay, and the subsequent period of constant intensity of Joaquin. Utilizing the SCDI analysis as the initial conditions for two versions of the COAMPS-TC model results in an accurate 72-h prediction of the interruption of the rapid decay and the period of constant intensity. Upscaling a similar SCDI analysis based on the 15-min interval AMVs provides a more realistic intensity and structure of Tropical Storm Joaquin for the initial conditions of the Navy Global Environmental Model (NAVGEM) than the synthetic TC vortex used operationally. This demonstration for a single 6-h period of AMVs indicates the potential for substantial impacts when an end-to-end cycling version is developed.
Abstract
A dynamic initialization assimilation scheme is demonstrated utilizing rapid-scan atmospheric motion vectors (AMVs) at 15-min intervals to simulate the real-time capability that now exists from the new generation of geostationary meteorological satellites. The impacts of these AMVs are validated with special Tropical Cyclone Intensity Experiment (TCI-15) datasets during 1200–1800 UTC 4 October leading up to a NASA WB-57 eyewall crossing of Hurricane Joaquin. Incorporating the AMV fields in the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI) COAMPS Dynamic Initialization (SCDI) means there are 30 and 90 time steps on the 15- and 5-km grids, respectively, during which the mass fields are adjusted to these AMV-based wind increments during each 15-min assimilation period. The SCDI analysis of the three-dimensional vortex structure of Joaquin at 1800 UTC 4 October closely replicates the vortex tilt analyzed from the High-Definition Sounding System (HDSS) dropwindsondes. Vertical wind shears based on the AMVs at 15-min intervals are well correlated with the extreme rapid decay, an interruption of that rapid decay, and the subsequent period of constant intensity of Joaquin. Utilizing the SCDI analysis as the initial conditions for two versions of the COAMPS-TC model results in an accurate 72-h prediction of the interruption of the rapid decay and the period of constant intensity. Upscaling a similar SCDI analysis based on the 15-min interval AMVs provides a more realistic intensity and structure of Tropical Storm Joaquin for the initial conditions of the Navy Global Environmental Model (NAVGEM) than the synthetic TC vortex used operationally. This demonstration for a single 6-h period of AMVs indicates the potential for substantial impacts when an end-to-end cycling version is developed.
